Seyed Morteza Moghimi ¹, Seyed Hossein Hosseinian ², Abolfazl Elahimanesh ³, Gholamreza Sarlak ⁴

Affiliations
1 Department of Electrical Engineering, Jasb Branch, Islamic Azad University, Jasb, IR
2 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, IR
3 Department of Electrical, Qom Branch, Jame Elmi Karbordi University, Qom, IR
4 Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IR

Abstract

Background/Objectives: Design of smart energy planning and management as a Smart Energy Management System (SEMS). Methods/Statistical Analysis: Approach used in this paper is that instead of using a mathematical modeling system based on statistical analysis, a kind of intelligent modeling has been adopted. The methodology ventures to achieve the variety of processes which produce the desired objective function being monitored, that is the simulation results obtained by GAMS.Software GAMS (v. 24.1.2) as software has been utilized for solving the given problem of optimization in this paper that is considered as one of problems of Mixed Integer Linear Programming (MILP) and it is linked by MATLAB software (v. 15.0.0.0) to display its graphic results. Findings: In order to achieve the aforementioned objective a sample Micro-Grid (MG) including a wide range of sources scattered as well as smart ways to manage and save energy are adopted. Furthermore, the paper includes a SEMS in order to optimize the operation of smart sample network, generation planning and energy saving design. Since, functions such as optimization are dependent on unit power generation and renewable resources units output, SEMS becomes essential for energy generation forecasts. Hence, applying a suitable stochastic predictive algorithm to the output products, which is primarily distributed based on wind and solar energy, is considered for short periods and hourly forecasts. Finally, the predicted input values, as the optimization necessitates, are applied. Based on the data output of predictive models representing the generation power and climatic conditions in the coming hours, the intelligent optimization of optimal operation patterns to suit the user have been chosen as objectives and are implemented. Applications/Improvements: Predicted input values, as the optimization necessitates, are applied. Based on the data output of predictive models representing generation power and climatic conditions in the coming hours, SEMS are implemented.

Keywords

Distributed Generation, Energy Storage, Intelligent Energy Management System, Micro Grid, Multi-Objective Optimization

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Hamed Aliyari ¹, Reza Effatnejad ², Mehdi Izadi ¹, Seyed Hossein Hosseinian ³, Masoud Shirkhani ¹

Affiliations
1 Electrical and Biomedical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IR
2 Engineering Department, Science and Research, Alborz Branch, Islamic Azad University, Karaj, IR
3 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, IR

Abstract

The reactive power distribution affects the security and the economic performance of the power system chiefly. Although the reactive power generation is in does not cost anything in the operation phase, but it affects the whole cost by the system losses. The mentioned problem is a non-linear optimization topic with the combination of continuous and noncontinuous variables. This paper proposes a new innovative method to reach the increase in the power system deviation and transmission loss. In this study, it is tried to reach the most optimum multiple goals by based on multi objective Genetic Algorithm with Pareto optimal non-dominated solutions of OPF problem. In this study had employed the limit accept block until for put variable to the allowable limits. Simulation result shows the effect of proposes algorithm in reducing transmission loss and voltage deviation. A MNSGA-II is presented in this paper to solve the multi objective problems. The main goal is to determine the optimal combination of power outputs for all generating units that optimize the total power loss and voltage deviations while satisfying load demand (active and reactive) and operating constraints. According to results, the ability to jump out of the local optima enhanced and thus the high precision are achieved.

Keywords

NSGA-II, Optimization, Power Loss, Reactive Power, Voltage Deviation.

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Abdullah Mahmoudi ¹, Seyed Hossein Hosseinian ¹

Affiliations
1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, IR

Abstract

Active rectifier loads become more popular, recently, and their characteristics have been studied in the literature especially when they are connected to Micro Grids (MGs). In this paper, a modified AC current controller for such loads is proposed where the current direction and voltage drops of the output filter of active loads are involved in the controller. The proposed controller improves the performance of such loads either in single mode or in combined mode with MGs. Active load dynamics become relatively fast and smooth using the proposed method. The modified AC current controller makes the system more stable and also possible choices for controller parameters are widened. The validation and comparison of different case studies show the performance of the proposed method.

Keywords

Active Loads, AC Current Controller, Eigenvalues, Microgrids, Stability Analysis

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